Define the new internet.
Look up the words people use online, add the ones we missed, and help make the internet easier to understand.
Look up the words people use online, add the ones we missed, and help make the internet easier to understand.
2,337 definitions
机器辅助翻译草稿 (Chinese) for "Vector Bias Audit": Vector Bias Audit is a ml review process that looks for uneven model behavior across groups or segments for numeric representation and similarity search. It uses slice metrics, representative data, and reviewer notes so teams can surface fairness risks while keeping evidence, reliability, and public-safe operational boundaries clear.
“示例草稿: The machine learning team used Vector Bias Audit when the vector store returned close matches, so the team could surface fairness risks before the model moved into evaluation.”
机器辅助翻译草稿 (Chinese) for "Dataset Model Card": Dataset Model Card is a ml documentation artifact that summarizes intended use, limits, and evaluation evidence for labeled and unlabeled data used for learning. It uses dataset notes, metric tables, and risk statements so teams can publish model behavior honestly while keeping evidence, reliability, and public-safe operational boundaries clear.
“示例草稿: The machine learning team used Dataset Model Card when the dataset received a new batch, so the team could publish model behavior honestly before the model moved into evaluation.”
机器辅助翻译草稿 (Chinese) for "Dataset Feature Store": Dataset Feature Store is a ml service that serves consistent features to training and inference for labeled and unlabeled data used for learning. It uses versioned feature definitions, freshness checks, and access policies so teams can avoid training-serving skew while keeping evidence, reliability, and public-safe operational boundaries clear.
“示例草稿: The machine learning team used Dataset Feature Store when the dataset received a new batch, so the team could avoid training-serving skew before the model moved into evaluation.”
机器辅助翻译草稿 (Chinese) for "Training Label Review": Training Label Review is a ml quality workflow that checks annotations for consistency and usefulness for model learning and optimization workflows. It uses agreement metrics, reviewer queues, and adjudication so teams can improve supervised learning data while keeping evidence, reliability, and public-safe operational boundaries clear.
“示例草稿: The machine learning team used Training Label Review when the training job restarted, so the team could improve supervised learning data before the model moved into evaluation.”
机器辅助翻译草稿 (Chinese) for "Training Feature Store": Training Feature Store is a ml service that serves consistent features to training and inference for model learning and optimization workflows. It uses versioned feature definitions, freshness checks, and access policies so teams can avoid training-serving skew while keeping evidence, reliability, and public-safe operational boundaries clear.
“示例草稿: The machine learning team used Training Feature Store when the training job restarted, so the team could avoid training-serving skew before the model moved into evaluation.”
机器辅助翻译草稿 (Chinese) for "Experiment Model Card": Experiment Model Card is a ml documentation artifact that summarizes intended use, limits, and evaluation evidence for controlled model comparison. It uses dataset notes, metric tables, and risk statements so teams can publish model behavior honestly while keeping evidence, reliability, and public-safe operational boundaries clear.
“示例草稿: The machine learning team used Experiment Model Card when the experiment showed a metric tradeoff, so the team could publish model behavior honestly before the model moved into evaluation.”
机器辅助翻译草稿 (Chinese) for "Label Provenance Ledger": Label Provenance Ledger is a ml record that tracks where data came from and how it changed for ground-truth or weak-supervision annotation. It uses hashes, source labels, and transformation history so teams can audit model inputs reliably while keeping evidence, reliability, and public-safe operational boundaries clear.
“示例草稿: The machine learning team used Label Provenance Ledger when the label set had disagreement, so the team could audit model inputs reliably before the model moved into evaluation.”
机器辅助翻译草稿 (Chinese) for "Experiment Data Split": Experiment Data Split is a ml experimental control that separates examples for training, validation, and testing for controlled model comparison. It uses randomization rules, leakage checks, and seed tracking so teams can measure generalization honestly while keeping evidence, reliability, and public-safe operational boundaries clear.
“示例草稿: The machine learning team used Experiment Data Split when the experiment showed a metric tradeoff, so the team could measure generalization honestly before the model moved into evaluation.”
机器辅助翻译草稿 (Chinese) for "Inference Drift Monitor": Inference Drift Monitor is a ml monitor that detects when data or predictions no longer match the training baseline for model prediction serving. It uses statistical tests, time windows, and alert thresholds so teams can respond before quality drops while keeping evidence, reliability, and public-safe operational boundaries clear.
“示例草稿: The machine learning team used Inference Drift Monitor when the endpoint handled burst traffic, so the team could respond before quality drops before the model moved into evaluation.”
机器辅助翻译草稿 (Chinese) for "Experiment Provenance Ledger": Experiment Provenance Ledger is a ml record that tracks where data came from and how it changed for controlled model comparison. It uses hashes, source labels, and transformation history so teams can audit model inputs reliably while keeping evidence, reliability, and public-safe operational boundaries clear.
“示例草稿: The machine learning team used Experiment Provenance Ledger when the experiment showed a metric tradeoff, so the team could audit model inputs reliably before the model moved into evaluation.”